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Data Analysis

This folder consists of data obtained from the experiments and the materials we use for analysis. Here we provide the following documents:

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Three well-known Border Gateway Anomalies (BGP) anomalies:
WannaCrypt, Moscow blackout, and Slammer, occurred in May 2017, May 2005, and January 2003, respectively.
The Route Views BGP update messages are publicly available from the University of Oregon Route Views Project and contain:
WannaCrypt, Moscow blackout, and Slammer: http://www.routeviews.org/routeviews/.

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The data include:

  • Demographic data of the participants including: gender, group of participation and number of years in the company.
  • Results of the use of Ethool including: expended time and subjective evaluation of if using a Likert of 5 points. Two different files are available corresponding to each iteration (prototype 1 and prototype 2).
  • Results of the SUS questionnaire for both iterations (prototype 1 and prototype 2).
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This code is provided here for research purpose(s) only. You are allowed to use this code/data provided that you cite the following papers:

 

R. Pilipović and P. Bulić, "On the Design of Logarithmic Multiplier Using Radix-4 Booth Encoding," in IEEE Access, vol. 8, pp. 64578-64590, 2020, doi: 10.1109/ACCESS.2020.2985345.

 

R. Pilipović, P. Bulić, and U.Lotrič, "A two-stage operand trimming approximate logarithmic multiplier" in IEEE Transactions on Circuits and Systems I: Regular Papers, 2021.

 

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We conduct to our knowledge a first measurement study of commercial 5G performance on smartphones by closely examining 5G networks of three carriers (two mmWave carriers, one mid-band 5G carrier) in three U.S. cities. We conduct extensive field tests on 5G performance in diverse urban environments. We systematically analyze the handoff mechanisms in 5G and their impact on network performance, and explore the feasibility of using location and possibly other environmental information to predict the network performance.

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This is the dataset of the experiment results of the ReuseTracker tool.

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Online Machine Learning for Energy-Aware Multicore Real-Time Embedded Systems Dataset is a Dataset composed of Hardware Performance Counters extracted from a Multicore Real-Time Embedded System. This Dataset encompasses every Monitorable Performance counters in a Cortex-A53 quad-core processor, totaling 54 performance counters, which are sampled periodically through a non-Intrusive Monitoring Framework implemented over Embedded Parallel Operating System (EPOS), a Real-Time Operating System.

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1. We selected the database for documentary analysis(Web of Science Core Collection).2. We set up a basic search ("BLOCKCHAIN" OR"BLOCK CHAINS" OR "BLOCK-CHAIN" OR "BLOCKCHAINS" OR "CHAIN OF BLOCKS" [TITLE]. All keywords were enclosed in quotation marks to ensure that they are not split during the retrieval process, except for "BLOCKCHAIN").3. We determined the relevant timespan (“2008” TO “2020” in the categories of “Custom year range”).4. We chose the relevant Citation Indexes (“SCI-EXPANDED” and “SSCI” and “A&HCI”).A total of 2573 publications were retrieved.

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This data is mainly used for the calibration of the long-stroke Cartesian robot.  The position data is measured by Leica Laser Tracker, including the data of the identification of kinematics parameters,  the data of the Gaussian process regression model validation and prediction, and the data that the compensation results based on HTM, HTM+SBD, HTM+GPR, HTM+SBD+GPR model in different position points.

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